Embodiments of the present invention relate to an asymmetrical memristor. Further embodiments relate to a neural network comprising an asymmetrical memristor. Some embodiments relate to a biohybrid synapse.
A central key element of information processing in the brain are the neurons comprising cell body's (soma), axons, dendrites and synapses. The electric stimulus transmission between neurons takes place via synapses. Neurotransmitter vesicles are emitted into the synaptic gap, defuse through the post-synaptic gap and specifically dock onto ion channel type anchor points. By docking, the configuration of the ion channel changes and it becomes permeable for ions as long as the neurotransmitters dock. Neuromorphic computing is based thereon and tries to abstractly describe the neuron as a mathematical model and realize the same as an electric circuit element in order to thus massively emulate switching processes of the brain in parallel [Mika Laiho and Eero Lehtonen, Cellular nanoscal network cell with memristors for local implication logic synapses, ISCAS, page 2051-2054. IEEE (2010)]. In the 90's, with the patch clamp method it was for the first time possible to prove how synaptic connections between neurons are strengthened or weakened by the time-relative firing performance. The learning rule is termed “spike timing dependent plasticity (STDP)”. The mechanism in biological systems is described in detail by a three-part synapses constellation between neurons and astrocyte [Roger Min, Thomas Nevian, Astrocyte signaling controls spike timing-dependent depression at neocortical synapses, Nature Neuroscience, Vol. 15, No. 5 May 2012].
The idea of the memristor was introduced more than 40 years ago by Leon Chua [Chua, L. O., Memristor—the missing circuit element, IEEE Trans. Circuit Theory (1971), 507-519.]. The memristor is a two-terminal passive device with a variable internal resistance. This resistance depends on the amount of charge which passed through the memristor by a bias applied before. As soon as the desired internal resistance is adjusted, this biasing is interrupted. The memristor will thus maintain exactly this internal resistance until the next biasing is applied. Recently, the memristor was discussed in literature in connection with synapses and neuro-morphological systems [Kuk-Hwan Kim, Siddharth Gaba, Dana Wheeler, Jose M. Cruz-Albrecht, Tahir Hussain, Narayan Srinivasa and Wei Lu, A Functional Hybrid Memristor, Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications, dx.doi.org/10.1021/n/203687n, Nano Lett. 2012, 12, 389-395.], [Duygu Kuzum, Rakesh G. D. Jeyasingh, Byoungil Lee, and H.-S. Philip Wong, Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing, Nano Letters, dx.doi.org/10.1021/n/201040y, 2011,], [John Paul Strachan, Dmitri B. Strukov, Julien Borghetti, J. Joshua Yong, Gilberto Medeiros-Ribeiro and R. Stanley Williams, The switching location of a bipolar memristor: chemical, thermal and structural mapping, Nanotechnology 22 (2011) 254015 (6pp) doi:10.1088/0957-4484/22/25/254015].
First physical realizations of conventional memristor structures based on thin film technologies were published in 2007 [Q. Wang, D. S. Shang, Z. H. Wu, L. D. Chen, X. M. LI: “Positive” and “negative” electric-pulse-induced reversible resistance switching effect in Pr0.7Ca0.3Mn03 films. In: Appl. Phys. A. 86, 2007, pp. 357-360]. In April 2008, researchers of the company Hewlett-Packard [HP Labs: Memristor found: HP Labs proves fourth integrated circuit element] presented a layered composite of titanium dioxide with platinum electrodes as a memristor having a relatively simple setup. At the end of August 2010, documents by Jun Yao of Rice University disclosed that also simple silicon dioxide works as a layer material [Heise-Newsticker: Memristor aus Siliziumoxid-Nanodrähten] [Mike Williams: Silicon oxide circuits break barrier (engl.)].
In the US 2004/0150010 A1 an array of nanowires is described, where at each crossing an input and an output wire cross. Using this system, “threshold functions”, i.e. preferably sigmoidal threshold value functions, may be simulated which however always provide the same symmetric response function [Shyam Prasad Adhlkari, Changju Yang, Hyongsuk Kim, Member, IEEE, and Leon O. Chua, Fellow, Memristor Bridge Synapse-Based Neural Network and its Learning, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 23, NO. 9, September 2012].
With an exact analysis of the biological data, it now turns out that a real biological neuronal network with its neurons and their synapses comprises a clearly more complex behavior than mentioned in the above-mentioned documents. Thus, in biological reality, the dynamic learning behavior of a synapse is only partially characterized by sigmoidal response behavior, as together with complex biochemistry an asymmetry in signal transmission (from axon to dendrite) exists which is not physically copied by the above devices, structures or circuitries. This technical deficiency of the introduced classic memristors finally leads to the fact that the so-called “spike-timing-dependent-plasticity (STDP)”, which is substantial for the learning behavior of neurons, may not be copied using those systems [Andrew Nere, Umberto Olcese, David Balduzzi, Giulio Tononi, A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP, PLoS ONE, Vol. 7, Issue 5, 1 May 2012, e36958].
All classical memristor architectures are thus based on symmetrical concepts as they are conventionally used by a person skilled in the art in the design of micro-electronic circuitries, as they are easily implemented and comprise a clear, symmetrical response behavior due to symmetry [Henry Markram, Wulfram Gerstner and Per Jesper Sjöström, A history of spike-timing-dependent plasticity, Frontiers in Synaptic Neuroscience, Vol. 3, Article 4, August 2011].
Even the simulation of neuronal networks with the help of analog, micro-electronical systems may do without a physical implementation of the asymmetries in physical realization [Spektrum der Wissenschaft, Karl-Heinz-Meier, September 2012].
Thus, such hardware implementations represent no possibility to directly interconnect with real biological neuron networks as they do not copy the function of real biological synapses (e.g., symmetry of the information flow, learning behavior). This means that, for example, a real biological neuron cannot feel the signal behavior on the opposite side of the hardware as the signals are in a non-compatible measurement range.
Therefore, it is the object of the present invention to provide a concept that enables to model or reproduce the functionality of a real biological synapse.